class_docstr: Sums several losses into one.
  
  Args:
      num_inputs: number of input losses
      weights: a list of coefficient for merging losses
code: def forward(self, **kwargs):
      values = [kwargs[x] for x in sorted(kwargs.keys())]
      loss = torch.zeros_like(values[0])
      for loss_idx, loss_value in enumerate(values):
          if self._weights is not None:
              loss = loss.add(loss_value, alpha=self._weights[loss_idx])
          else:
              loss = loss.add(loss_value)
      return loss
  
file_path: NeMo/nemo/collections/common/losses/aggregator.py
imports: from typing import List
  import torch
  from nemo.core.classes import Loss, typecheck
  from nemo.core.neural_types import LossType, NeuralType
label_desc: Use image node_id 4 for calling the AggregatorLoss obj's underlying member method code with example usage: obj.forward() and returns: loss